--- license: cc-by-nc-nd-4.0 language: - en tags: - histology - pathology - vision - pytorch extra_gated_prompt: >- The data and associated code are released under the CC-BY-NC 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. If you are a commercial entity, please contact the corresponding author. extra_gated_fields: Full name (first and last): text Current affiliation (no abbreviations): text Type of Affiliation: type: select options: - Academia - Industry - label: Other value: other Current and official institutional email (**this must match your primary email in your Hugging Face account, @gmail/@hotmail/@qq email domains will be denied**): text Please explain your intended research use: text I agree to all terms outlined above: checkbox I agree to use this model for non-commercial, academic purposes only: checkbox I agree not to distribute the model, if another user within your organization wishes to use Patho-Bench data, they must register as an individual user: checkbox metrics: - accuracy pipeline_tag: image-feature-extraction library_name: timm --- # ♆ Patho-Bench [📄 Preprint](https://arxiv.org/pdf/2502.06750) | [Code](https://github.com/mahmoodlab/Patho-Bench) Patho-Bench **Patho-Bench is designed to evaluate patch and slide encoder foundation models for whole-slide images (WSIs).** This HuggingFace repository contains the data splits for the public Patho-Bench tasks. Please visit our codebase on [GitHub](https://github.com/mahmoodlab/Patho-Bench) for the full codebase and benchmark implementation. This project was developed by the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital. This work was funded by NIH NIGMS R35GM138216. > [!NOTE] > Contributions are welcome! If you'd like to submit a new dataset and/or task for inclusion in Patho-Bench, please reach out to us via the [Issues](https://github.com/mahmoodlab/Patho-Bench/issues) tab of our Github repo. Currently, Patho-Bench contains the following task families. We will add more tasks in the future. For further details on each task, please refer to the [THREADS foundation model paper](https://arxiv.org/abs/2501.16652). | **Family** | **Description** | **Tasks** | |--------------------------------------|---------------------------------------------------------------------------------------|----------| | **Morphological Subtyping** | Classifying distinct morphological patterns associated with different disease subtypes | 4 | | **Tumor Grading** | Assigning a grade based on cellular differentiation and growth patterns | 2 | | **Molecular Subtyping** | Predicting antigen presence (e.g., via IHC staining) | 3 | | **Mutation Prediction** | Predicting specific genetic mutations in tumors | 21 | | **Treatment Response & Assessment** | Evaluating patient response to treatment | 6 | | **Survival Prediction** | Predicting survival outcomes and risk stratification | 6 | ## 🔥 Latest updates - **February 2025**: Patho-Bench is now available on HuggingFace. ## ⚡ Installation Install the required packages: ``` pip install --upgrade datasets pip install --upgrade huggingface_hub ``` ## 🔑 Authentication ```python from huggingface_hub import login login(token="YOUR_HUGGINGFACE_TOKEN") ``` ## ⬇️ Usage The Patho-Bench data splits are designed for use with the Patho-Bench [software package](https://github.com/mahmoodlab/Patho-Bench). However, you are welcome to use the data splits in your custom pipeline. Each task is associated with a YAML file containing task metadata and a TSV file containing the sample IDs, slide IDs, and labels. > [!NOTE] > Patho-Bench only provides the data splits and labels, NOT the raw image data. You will need to download the raw image data from the respective dataset repositories (see links below). ### Download an individual task ```python import datasets dataset='cptac_coad' task='KRAS_mutation' datasets.load_dataset( 'MahmoodLab/Patho-Bench', cache_dir='/path/to/saveto', dataset_to_download=dataset, # Throws error if source not found task_in_dataset=task, # Throws error if task not found in dataset trust_remote_code=True ) ``` ### Download all tasks from a dataset ```python import datasets dataset='cptac_coad' task='*' datasets.load_dataset( 'MahmoodLab/Patho-Bench', cache_dir='/path/to/saveto', dataset_to_download=dataset, task_in_dataset=task, trust_remote_code=True ) ``` ### Download entire Patho-Bench [4.2 MB] ```python import datasets dataset='*' datasets.load_dataset( 'MahmoodLab/Patho-Bench', cache_dir='/path/to/saveto', dataset_to_download=dataset, trust_remote_code=True ) ``` ## 📢 Image data access links For each dataset in Patho-Bench, please visit the respective repository below to download the raw image data. | Dataset | Link | |---------|------| | EBRAINS [Roetzer et al., 2022] | [https://doi.org/10.25493/WQ48-ZGX](https://doi.org/10.25493/WQ48-ZGX) | | BRACS [Brancati et al., 2021] | [https://www.bracs.icar.cnr.it/](https://www.bracs.icar.cnr.it/) | | PANDA [Bulten et al., 2022] | [https://panda.grand-challenge.org/data/](https://panda.grand-challenge.org/data/) | | IMP [Neto et al., 2024] | [https://rdm.inesctec.pt/dataset/nis-2023-008](https://rdm.inesctec.pt/dataset/nis-2023-008) | | BCNB [Xu et al., 2021] | [https://bupt-ai-cz.github.io/BCNB/](https://bupt-ai-cz.github.io/BCNB/) | | CPTAC-BRCA [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-brca/](https://www.cancerimagingarchive.net/collection/cptac-brca/) | | CPTAC-CCRCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-ccrcc/](https://www.cancerimagingarchive.net/collection/cptac-ccrcc/) | | CPTAC-COAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-coad/](https://www.cancerimagingarchive.net/collection/cptac-coad/) | | CPTAC-GBM [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-gbm/](https://www.cancerimagingarchive.net/collection/cptac-gbm/) | | CPTAC-HNSC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-hnsc/](https://www.cancerimagingarchive.net/collection/cptac-hnsc/) | | CPTAC-LSCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-lscc/](https://www.cancerimagingarchive.net/collection/cptac-lscc/) | | CPTAC-LUAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-luad/](https://www.cancerimagingarchive.net/collection/cptac-luad/) | | CPTAC-PDAC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-pda/](https://www.cancerimagingarchive.net/collection/cptac-pda/) | | MUT-HET-RCC | [https://doi.org/10.25452/figshare.plus.c.5983795](https://doi.org/10.25452/figshare.plus.c.5983795) | | OV-Bevacizumab [Wang et al., 2022] | [https://www.nature.com/articles/s41597-022-01127-6](https://www.nature.com/articles/s41597-022-01127-6) | | NADT-Prostate [Wilkinson et al., 2021] | [https://www.medrxiv.org/content/10.1101/2020.09.29.20199711v1.full](https://www.medrxiv.org/content/10.1101/2020.09.29.20199711v1.full) | | POST-NAT-BRCA | [https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244) | | BOEHMK | [https://www.synapse.org/Synapse:syn25946117/wiki/611576](https://www.synapse.org/Synapse:syn25946117/wiki/611576) | | MBC | [https://www.synapse.org/Synapse:syn59490671/wiki/628046](https://www.synapse.org/Synapse:syn59490671/wiki/628046) | | SURGEN | [https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285) / [arXiv](https://arxiv.org/abs/2502.04946) | ## 📇 Contact For any questions, contact: - Faisal Mahmood (faisalmahmood@bwh.harvard.edu) - Anurag Vaidya (avaidya@mit.edu) - Andrew Zhang (andrewzh@mit.edu) - Guillaume Jaume (gjaume@bwh.harvard.edu) ## 📜 Data description Developed by: Mahmood Lab AI for Pathology @ Harvard/BWH Repository: GitHub License: CC-BY-NC-4.0 ## 🤝 Acknowledgements Patho-Bench tasks were compiled from public image datasets and repositories (linked above). We thank the authors of these datasets for making their data publicly available. ## 📰 How to cite If Patho-Bench contributes to your research, please cite: ``` @article{vaidya2025molecular, title={Molecular-driven Foundation Model for Oncologic Pathology}, author={Vaidya, Anurag and Zhang, Andrew and Jaume, Guillaume and Song, Andrew H and Ding, Tong and Wagner, Sophia J and Lu, Ming Y and Doucet, Paul and Robertson, Harry and Almagro-Perez, Cristina and others}, journal={arXiv preprint arXiv:2501.16652}, year={2025} } @article{zhang2025standardizing, title={Accelerating Data Processing and Benchmarking of AI Models for Pathology}, author={Zhang, Andrew and Jaume, Guillaume and Vaidya, Anurag and Ding, Tong and Mahmood, Faisal}, journal={arXiv preprint arXiv:2502.06750}, year={2025} } ```